{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For if you want to just be working in your cloned git directory, and not installed to a python environment\n",
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.insert(0, module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import ccd\n",
    "from test.shared import read_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = {'QA_BITPACKED': False,\n",
    "              'QA_FILL': 255,\n",
    "              'QA_CLEAR': 0,\n",
    "              'QA_WATER': 1,\n",
    "              'QA_SHADOW': 2,\n",
    "              'QA_SNOW': 3,\n",
    "              'QA_CLOUD': 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n",
      "d:\\日常\\CCDC\\pyCCD\\lcmap-pyccd-develop\\ccd\\models\\robust_fit.py:96: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  beta, _, _, _ = numpy.linalg.lstsq(Xw, yw)\n"
     ]
    }
   ],
   "source": [
    "data = read_data('test/resources/test_3657_3610_observations.csv')\n",
    "dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas = data\n",
    "results = ccd.detect(dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas, params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\envs\\python_37\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Date: 1982-12-04 00:00:00\n",
      "End Date: 2014-11-02 00:00:00\n",
      "\n",
      "Result: 0\n",
      "Start Date: 1984-05-23 00:00:00\n",
      "End Date: 1993-06-01 00:00:00\n",
      "Break Date: 1993-06-17 00:00:00\n",
      "QA: 8\n",
      "Norm: 2600.388381403098\n",
      "\n",
      "Change prob: 1.0\n",
      "Result: 1\n",
      "Start Date: 1994-04-01 00:00:00\n",
      "End Date: 2003-07-15 00:00:00\n",
      "Break Date: 2003-07-23 00:00:00\n",
      "QA: 8\n",
      "Norm: 3449.7500478604525\n",
      "\n",
      "Change prob: 1.0\n",
      "Result: 2\n",
      "Start Date: 2005-08-21 00:00:00\n",
      "End Date: 2010-03-20 00:00:00\n",
      "Break Date: 2010-03-28 00:00:00\n",
      "QA: 8\n",
      "Norm: 1969.5973410403606\n",
      "\n",
      "Change prob: 1.0\n",
      "Result: 3\n",
      "Start Date: 2010-06-16 00:00:00\n",
      "End Date: 2012-08-16 00:00:00\n",
      "Break Date: 2013-05-23 00:00:00\n",
      "QA: 8\n",
      "Norm: 2058.638829943369\n",
      "\n",
      "Change prob: 1.0\n",
      "Result: 4\n",
      "Start Date: 2013-05-23 00:00:00\n",
      "End Date: 2014-10-09 00:00:00\n",
      "Break Date: 2014-10-09 00:00:00\n",
      "QA: 24\n",
      "Norm: 0.0\n",
      "\n",
      "Change prob: 0.0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 4800x2700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.array(results['processing_mask'], dtype=np.bool)\n",
    "print('Start Date: {0}\\nEnd Date: {1}\\n'.format(datetime.fromordinal(dates[0]),\n",
    "                                                datetime.fromordinal(dates[-1])))\n",
    "\n",
    "predicted_values = []\n",
    "prediction_dates = []\n",
    "break_dates = []\n",
    "start_dates = []\n",
    "\n",
    "for num, result in enumerate(results['change_models']):\n",
    "    print('Result: {}'.format(num))\n",
    "    print('Start Date: {}'.format(datetime.fromordinal(result['start_day'])))\n",
    "    print('End Date: {}'.format(datetime.fromordinal(result['end_day'])))\n",
    "    print('Break Date: {}'.format(datetime.fromordinal(result['break_day'])))\n",
    "    print('QA: {}'.format(result['curve_qa']))\n",
    "    print('Norm: {}\\n'.format(np.linalg.norm([result['green']['magnitude'],\n",
    "                                            result['red']['magnitude'],\n",
    "                                            result['nir']['magnitude'],\n",
    "                                            result['swir1']['magnitude'],\n",
    "                                            result['swir2']['magnitude']])))\n",
    "    print('Change prob: {}'.format(result['change_probability']))\n",
    "    \n",
    "    days = np.arange(result['start_day'], result['end_day'] + 1)\n",
    "    prediction_dates.append(days)\n",
    "    break_dates.append(result['break_day'])\n",
    "    start_dates.append(result['start_day'])\n",
    "    \n",
    "    intercept = result['green']['intercept']\n",
    "    coef = result['green']['coefficients']\n",
    "    \n",
    "    predicted_values.append(intercept + coef[0] * days +\n",
    "                            coef[1]*np.cos(days*1*2*np.pi/365.25) + coef[2]*np.sin(days*1*2*np.pi/365.25) +\n",
    "                            coef[3]*np.cos(days*2*2*np.pi/365.25) + coef[4]*np.sin(days*2*2*np.pi/365.25) +\n",
    "                            coef[5]*np.cos(days*3*2*np.pi/365.25) + coef[6]*np.sin(days*3*2*np.pi/365.25))\n",
    "    \n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fg = plt.figure(figsize=(16,9), dpi=300)\n",
    "a1 = fg.add_subplot(2, 1, 1, xlim=(min(dates), max(dates)), ylim=(-5000, 5000))\n",
    "\n",
    "# Predicted curves\n",
    "for _preddate, _predvalue in zip(prediction_dates, predicted_values):\n",
    "    a1.plot(_preddate, _predvalue, 'orange', linewidth=1)\n",
    "\n",
    "a1.plot(dates[mask], greens[mask], 'g+') # Observed values\n",
    "a1.plot(dates[~mask], greens[~mask], 'k+') # Observed values masked out\n",
    "for b in break_dates: a1.axvline(b)\n",
    "for s in start_dates: a1.axvline(s, color='r')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "interpreter": {
   "hash": "8674740e891edb96ec1c0c3bb593e6c3298b17c9ae0b00901ed5084595138999"
  },
  "kernelspec": {
   "display_name": "Python [conda env:lcmap-pyccd]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
